IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i4p47-d791836.html
   My bibliography  Save this article

Dataset: Roundabout Aerial Images for Vehicle Detection

Author

Listed:
  • Enrique Puertas

    (Department of Science, Computing and Technology, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain)

  • Gonzalo De-Las-Heras

    (SICE Canada Inc., Toronto, ON M4P 1G8, Canada)

  • Javier Fernández-Andrés

    (Department of Engineering, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain)

  • Javier Sánchez-Soriano

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain)

Abstract

This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Suggested Citation

  • Enrique Puertas & Gonzalo De-Las-Heras & Javier Fernández-Andrés & Javier Sánchez-Soriano, 2022. "Dataset: Roundabout Aerial Images for Vehicle Detection," Data, MDPI, vol. 7(4), pages 1-11, April.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:4:p:47-:d:791836
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/4/47/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/4/47/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:7:y:2022:i:4:p:47-:d:791836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.